Lena Hall: When AI Makes Everything Buildable, Trust Becomes the Only Moat — BigGo Finance

One engineer at a recent AI conference told Lena Hall that the opportunity cost of not working 9 a.m. to 9 p.m., six days a week, felt too high. The pressure comes from a simple observation: everyone now has access to the same models, the same reasoning capacity, and the same ability to ship features this afternoon. But Hall, an engineer and go-to-market operator who has worked with Y Combinator companies and now sits at Akamai, argues that this anxiety stems from a category error. The superpower of “being good at using AI” has expired, not because AI got less useful, but because it became an abundant resource. In a conference talk on the AI Engineer podcast, she laid out a framework for what actually matters when anything can be built: the signal layer.

The Convergence Machine and the Collapse of the Average

Hall’s central claim is blunt: AI is a “really smart convergence machine.” Left alone, it makes everything the same. The mechanism is measurable. Anything that can be graded can be trained against. She cites investor Sarah Guo’s formulation — “a compiler is a free grader, a test suite is a free grader” — to explain why code automation converged first. Writing and shipping software is the most checkable task in existence.

The numbers tell the story. Two years ago, the best autonomous coding agents solved a fraction of tasks on the standard software engineering benchmark. Now the best agents score in the high eighties — nearly a tripling of measured capability. But Hall’s point is that the benchmark measures only the part of software engineering that has a grader. Shipping is where all the ungraded parts come back in.

“Anything that you can measure you can train against. A compiler is a free grader. A test suite is a free grader. And the instant a task can grade itself you can grind a model against you know that grade until you win.”

The strategic consequence is uncomfortable. Implementation is converging for free for everyone, at the same time, and the most buildable thing and the most valuable thing are almost never the same thing. The cost of average has gone to zero, and so has its value. Pointing — deciding what to build — was always the real job. Implementation work just used to be so voluminous that nobody had to get good at it.

The Limits of Taste and the Value of the Weird

So where do you point? Hall turns to Paul Graham for the first part of the answer: find what people genuinely want by feeling the need yourself. Build something you and your friends need, because the market hasn’t formed yet, surveys can’t see it, and your own need is the only signal that isn’t a “crap signal.” The best ideas may sound genuinely lame at first. She cites the example of a guy with a camera strapped to his head live-streaming his life — ridiculous at the time, and it became Twitch.

But she immediately dismantles the tempting fallback of “good judgment and good taste” as a moat. Taste, she argues, is just preference under feedback, and preference under feedback is exactly what these systems can learn. Anything you can demonstrate enough times with a better-or-worse signal attached, the machine can eventually imitate. Broad good taste is not a differentiator.

“Taste is really just preference under feedback, and preference under feedback is exactly what these systems can learn.”

What actually resists training is narrower and more durable. Hall names two things: taste and judgment about what hasn’t happened yet — there is no data for an event that hasn’t occurred — and taste and judgment embedded in a relationship the model can’t observe. The model has read everything ever written about your customer, but it has never met them.

She grounds this in Richard Hamming’s famous study of why some scientists did great work while equally smart peers didn’t. The great ones worked on important problems — not problems that merely sound impressive, but problems where they had a reasonable attack. Time travel is consequential, Hamming would say, but not important, because nobody has an attack on it. AI just handed everyone an attack on everything. The rarest thing is no longer having an attack; it is knowing which problem is worth attacking.

“You don’t actually need to be first. You just need to be genuinely close to a problem you actually understand where your insight is in the delta between what AI has been trained on and what should exist.”

That judgment comes from being a real person close to a real domain, with your own battle scars, your weirdly specific experience, the thing you care about more than is reasonable. The convergence machine will not proactively propose weird, specific, genuinely embarrassing ideas.

The Ship Side: Content, Sameness, and Two Ways to Use AI

Knowing your signal is only half the job. The other half is getting it from your head into the head of the person it was meant for — and most people do that with content. Here the convergence machine has already done its damage.

Hall observes that over the last two years, every feed has started to sound the same. The same LinkedIn posts, the same three bullet points and a bold takeaway, the same polished blog post that says nothing. Readers can now pattern-match AI in half a second. If a model could have written your post from a one-line prompt, the reader’s brain skips it for the same reason. AI has learned the algorithm, the format that performs, what gets clicks — and everyone wants to hand the machine a paragraph and say “make it viral, make me rich.” It fills every gap you leave with sameness.

The fix is to distinguish two ways of using the machine that look identical from the outside:

The Three Places Signal Distorts on the Way Out

Even with a clear signal, Hall says it falls apart between your brain and your users’ understanding in three distinct places, each with a different fix depending on product type and company size.

Source distortion is common in startups. Founders know the signal so well they compress it past legibility. They assume context the audience doesn’t have, and the room hears something technically cool without understanding why it matters. Hall describes helping a Y Combinator company with exactly this. The founders were brilliant, the product was genuinely new, but every pitch started with architecture and clever parts they were proud of. It landed as noise because the customer pain had been deleted from the story. They rewrote the opening to include the thing the user hated, and the same product, in the same week, converted the next conversations into pilots. They then turned that into a repeatable go-to-market system.

Organization distortion hits almost every big company. As signal travels through layers of management, legal, sales, and every department, at every handoff it gets rewound toward the average. Hall is explicit that this is not incompetence — it’s investment. Hand a founder and a person three layers down the same task and the same AI, and you get two different things. The founder sweats the unaverageable details because the outcome is theirs. Others ship to spec, close Jira tickets, and answer for compliance rather than conviction. A long delegation chain plus a convergence machine is, in her words, “really a factory for automating the signal out of your own company.”

The first instinct — adding more process — is wrong. It adds layers, bureaucracy, and slows everything down. The fix is to reattach the signal to the outcome like a founder, adding a very thin signal layer to go-to-market engineering whose only job is to validate and carry the original intent across handoffs intact.

Machine distortion is the third failure mode. You write one careful launch with your claim, evidence, and scope clearly stated, and then AI remixes it into a tweet, a sales deck, a partner one-pager. A single narrow eval that scored 94% gets repeated enough times that customers hear it as a promise. The number survives; the scope dies.

Engineering the Signal Layer: A Worked Example

Hall’s prescription is a thin, deliberate function whose job is to make sure what users take away is still the specific thing you meant. She walks through a concrete example: a monitoring tool.

There are twelve other tools in the category, but this one does something different — it tells you what not to wake up for. It stays quiet on the noise, so when you get paged at night, you believe it. That quiet, that trust earned by silence, is the signal.

The three-step engineering process:

1. Say it in one sentence with the limit built in. Not “intelligent AI-native observability platform,” but something like: “Stays quiet on anything it can’t tie to a real user impact and shows you everything it silenced so you can overrule it.” The promise and the scope are welded together.
2. Make sure the limit can’t be edited out. In the product, every suppressed alert is visible. In the launch, statements like “90% fewer pages” live next to statements like “every silence is visible and reversible.” This matters because when AI chops your launch into a tweet, it can keep the impressive number but strip the part that keeps the product honest.
3. Before you scale, check what people actually heard. Give a readme to an SRE who has never seen your project and ask them to describe the product back to you. The gap between what they say and what you meant is the distortion you were about to broadcast.

This signal layer is lightweight and largely buildable. Hall says you can automate more of the checking, catching, and surveying than most people realize.

Trust as the Last Moat

The entire exercise — building, shipping, undistorted signal — serves one thing: getting a human, or increasingly an agent, to choose you and rely on you when there are infinite identical-looking alternatives. That is trust. And trust is the one thing left with no grader.

“There’s no benchmark for it, no reward signal. It can’t be entirely automated because it’s granted slowly through relationship with consent.”

She offers the example of doctors who open one particular tool every morning — that habit was not trained into them. It was earned. And she warns that getting the signal wrong is not neutral; it’s negative. Producing averageness costs real money — tokens, infrastructure, salaried hours of good people. Customers who look at your product once, decide once, and never come back. Every generic post teaches them your name isn’t worth the click. You spend real money to make yourself harder to choose.

The deepest tension Hall identifies is that AI has simultaneously made everything easier and made the differentiators harder to see. The answer is not to out-optimize the machine — that race is lost, because the machine optimizes faster. The answer is to occupy the two positions the machine structurally cannot reach: judgment about events that haven’t happened yet, and judgment embedded in relationships it cannot observe. Both require being a real person, close to a real domain, with a stake in the outcome. For operators, the scarce resource is no longer implementation capacity but conviction, and the highest-leverage investment is not more AI tooling but a thin, deliberate system for defining and protecting what makes your version specifically yours.